A dynamic probabilistic (stochastic) multivariate model of disease related variables has been developed. The implications of this model for cross-sectional data will be investigated under the assumptions: a) The study population is in equilibrium. b) The mean motion (non-diffusive) is determined by the gradient of a potential function. c) The diffusive motion is independent in the components. From these assumptions, the homeostatic matrix is similar to the matrix of n-2 order partial correlations. This will be tested. The problem of simultaneously diagnosing a set of patients with well-defined indicants and defining a set of etiologies to explain these indicants is a problem of enormous computational complexity. The attempt will be made to place this problem within the echelons of computational complexity developed by computer scientists. Algorithms for the solution of this formal disease definition problem will be characterized and the difficulty of a computational solution assessed. A method for the determination of the Markov Network of a set of normally distributed variables has been derived on the basis of some approximations. The extent to which these approximations distort the conclusions will be evaluated in a sampling study. A GOM ("Grade of Membership") model based on the theory of fuzzy sets has been developed and successfully applied to preoperative Tetralogy of Fallot patients. An extension of the model to staging will be sought. BIBLIOGRAPHIC REFERENCE: Spees, E.K., Pool, P., Sullinger, W.O., Clark, G.B., Passerati, F.A., Sperry, D., Woodbury, Max A. and D.B. Amos. "HL-Genetic Structure of an Eritrean Semitic Group." Histocompatibility Testing 1975, Munksgaard, Copenhagen, 1976, pp. 213-218.